Virtual labs are emerging as a key component in the construction of future digital environments. In the environmental science community, most existing virtual labs focus on the problem of integrating often complex and heterogeneous data. In this project, we seek to enhance virtual labs with sophisticated methodological capability, embracing state-of-the-art data science techniques to assist in the societally-relevant interpretation of these data. In this feasibility study, we focus on one particular family of data science techniques, that is, changepoint detection methods. Such methods are designed to identify fundamental changes and anomalous behaviour in data, typically within time-series, but also applicable across space and time and to complex, multivariate problems. The project will build on the rich, complex, multi-faceted data available from the Environmental Change Network (ECN), that offers detailed multivariate 25-year long data sets for a range of ecosystems in the UK.